Literature DB >> 22036894

Clinical implementation of a GPU-based simplified Monte Carlo method for a treatment planning system of proton beam therapy.

R Kohno1, K Hotta, S Nishioka, K Matsubara, R Tansho, T Suzuki.   

Abstract

We implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer-unified device architecture platform developed by NVIDIA. The GPU-based SMC was clinically applied for four patients with head and neck, lung, or prostate cancer. The results were compared to those obtained by a traditional CPU-based SMC with respect to the computation time and discrepancy. In the CPU- and GPU-based SMC calculations, the estimated mean statistical errors of the calculated doses in the planning target volume region were within 0.5% rms. The dose distributions calculated by the GPU- and CPU-based SMCs were similar, within statistical errors. The GPU-based SMC showed 12.30-16.00 times faster performance than the CPU-based SMC. The computation time per beam arrangement using the GPU-based SMC for the clinical cases ranged 9-67 s. The results demonstrate the successful application of the GPU-based SMC to a clinical proton treatment planning.

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Year:  2011        PMID: 22036894     DOI: 10.1088/0031-9155/56/22/N03

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  13 in total

Review 1.  Treatment planning for proton therapy: what is needed in the next 10 years?

Authors:  Hakan Nystrom; Maria Fuglsang Jensen; Petra Witt Nystrom
Journal:  Br J Radiol       Date:  2019-08-07       Impact factor: 3.039

Review 2.  GPU-based high-performance computing for radiation therapy.

Authors:  Xun Jia; Peter Ziegenhein; Steve B Jiang
Journal:  Phys Med Biol       Date:  2014-02-03       Impact factor: 3.609

3.  Dosimetric feasibility of real-time MRI-guided proton therapy.

Authors:  M Moteabbed; J Schuemann; H Paganetti
Journal:  Med Phys       Date:  2014-11       Impact factor: 4.071

Review 4.  Adaptive proton therapy.

Authors:  Harald Paganetti; Pablo Botas; Gregory C Sharp; Brian Winey
Journal:  Phys Med Biol       Date:  2021-11-15       Impact factor: 3.609

5.  GPU-based fast Monte Carlo dose calculation for proton therapy.

Authors:  Xun Jia; Jan Schümann; Harald Paganetti; Steve B Jiang
Journal:  Phys Med Biol       Date:  2012-11-06       Impact factor: 3.609

6.  Development and validation of the Dynamic Collimation Monte Carlo simulation package for pencil beam scanning proton therapy.

Authors:  Nicholas P Nelson; Wesley S Culberson; Daniel E Hyer; Theodore J Geoghegan; Kaustubh A Patwardhan; Blake R Smith; Ryan T Flynn; Jen Yu; Suresh Rana; Alonso N Gutiérrez; Patrick M Hill
Journal:  Med Phys       Date:  2021-04-09       Impact factor: 4.506

7.  Fast Monte Carlo simulation for total body irradiation using a (60)Co teletherapy unit.

Authors:  Xiaodong Liu; Danielle Lack; Joseph T Rakowski; Cory Knill; Michael Snyder
Journal:  J Appl Clin Med Phys       Date:  2013-05-06       Impact factor: 2.102

8.  A simplified Monte Carlo algorithm considering large-angle scattering for fast and accurate calculation of proton dose.

Authors:  Taisuke Takayanagi; Shusuke Hirayama; Shinichiro Fujitaka; Rintaro Fujimoto
Journal:  J Appl Clin Med Phys       Date:  2017-11-27       Impact factor: 2.102

9.  Fast Pencil Beam Dose Calculation for Proton Therapy Using a Double-Gaussian Beam Model.

Authors:  Joakim da Silva; Richard Ansorge; Rajesh Jena
Journal:  Front Oncol       Date:  2015-12-18       Impact factor: 6.244

10.  A Comparison Between GATE and MCNPX Monte Carlo Codes in Simulation of Medical Linear Accelerator.

Authors:  Hamid-Reza Sadoughi; Shahrokh Nasseri; Mahdi Momennezhad; Hamid-Reza Sadeghi; Mohammad-Hossein Bahreyni-Toosi
Journal:  J Med Signals Sens       Date:  2014-01
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